Forecasting Ionospheric foF2 Using Bidirectional LSTM and Attention Mechanism

The critical frequency of ionospheric F2 layer (foF2) is an important ionospheric characteristic parameter. In this paper, a deep learning model based on Bidirectional long short-term memory (BiLSTM) and attention mechanism is implemented for predicting the foF2 parameter. The inputs of models are the foF2 of globally available ionospheric ionosonde stations, geographic longitude and latitude, world time (UT), geomagnetic activity index, and solar activity index from 2015 to 2017. The superiority of the model is analyzed from different latitudes, seasons, and geomagnetic conditions. The results show that the prediction performance of the Bidirectional long short-term memory model based on attention mechanism (BiLSTM-Attention) is better than other models. The performance of the prediction model is optimal at high latitudes. The root mean square error (RMSE) and correlation coefficient (R) of the BiLSTM-Attention model are 0.539 MHZ and 0.908 MHz at high latitudes, respectively. In terms of RMSE, it is 25.243%, 18.209%, and 11.203% lower than those of the international reference ionosphere (IRI), LSTM, and BiLSTM models, respectively. The prediction results of the four seasons show that the models are more applicable in winter. Compared with the IRI model, the RMSE of the BiLSTM-Attention model in spring, summer, autumn, and winter is reduced by 24.344%, 21.181%, 25.058%, and 30.948%, respectively. The prediction effect of the BiLSTM-Attention model is improved in the magnetic quiet period, the magnetic moderate period and the magnetic storm period. Also, the improvement effect is more obvious in the magnetostatic day, and the RMSE is reduced by 27.462% compared with the IRI model.

Automated summary

This paper presents a deep learning model based on Bidirectional long short-term memory (BiLSTM) and attention mechanism for predicting the critical frequency of the ionospheric F2 layer (foF2). The model outperforms other models and shows the best performance at high latitudes and during winter. The model's prediction performance is also improved under different geomagnetic conditions.